Scientia Agricultura Sinica ›› 2024, Vol. 57 ›› Issue (4): 663-678.doi: 10.3864/j.issn.0578-1752.2024.04.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Remote Sensing Monitoring of Cropping Patterns Based on Phenology Information Atlas

TAO JianBin1(), WANG Yun1, ZHANG XinYue1, JIANG QiYue1, WU WenBin2()   

  1. 1 College of Urban and Environmental Sciences, Central China Normal University/Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Wuhan 430079
    2 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Beijing 100081
  • Received:2023-03-16 Accepted:2023-07-10 Online:2024-02-16 Published:2024-02-20
  • Contact: WU WenBin

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Abstract:

【Objective】 Cropping patterns are a summary of crop sequence which reflects the use patterns and efficiency of cropland resources. Through analyzing the phenological differences of different cropping patterns, the cropland phenology information atlas and cropping pattern spectrum for croplands on the Jianghan Plain were constructed, and the major cropping patterns in this area were mapped. 【Method】 The cropland phenology information atlas including different cropland use patterns was formed through expressing graphically the spatial difference between vegetation index states and cropland use patterns, according to the prior knowledge of crop planting and the phenological differences of different cropping patterns under the framework of geo-information atlas. Taking the major cropping patterns on the Jianghan Plain as the study cases, the vegetation index states in the key phenological periods were arranged and combined to establish the information remapping rule from the vegetation index states to the cropping patterns, their phenological characteristics were explored, and the cropping pattern spectrum was constructed. Then the data during the key phenological periods and phenological knowledge were integrated to map cropping patterns on the Jianghan Plain by using the Naive Bayes Networks. The vegetation index states of the key phenological periods were quantitatively expressed by using the knowledge probability coding method.【Result】The cropping pattern spectrum on the Jianghan Plain was constructed, and it was found that the cropping pattern spectrum on the Jianghan Plain was composed of eight cropping patterns: Spring single-cropping, Summer single-cropping, Spring-and-Summer double-cropping, Summer-and-Autumn double-cropping, double-cropping paddy-rice, cash crops, aquaculture ponds, trees or abandoned croplands. The results showed that the proposed cropping pattern spectrum and the method of mapping cropping patterns based on the key phenological periods and the Naive Bayesian Networks could extract all cropping patterns accurately, at the same time, which had good performance and suitability. There was a significant trend of the expansion of Summer-and-Autumn double-cropping and the shrink of Spring-and-Summer double-cropping and the Summer single-cropping on the Jianghan Plain during the study period.【Conclusion】The cropping pattern spectrum gave a picture of the overall situation of intensive utilization of croplands on the Jianghan Plain, for improving the accuracy of monitoring the use of croplands and enriching the connotation of the use of cropland resources.

Key words: phenology information atlas, cropping pattern, cropping pattern spectrum, Bayesian Network, the Jianghan Plain

Fig. 1

The major land-cover types (a) and sampling point (b) on the Jianghan Plain a: The data was sourced from FROM-GLC 2017 land-cover data; b: The data was sourced from NASA SRTM Digital Elevation data. A-E were the five validation areas representing typical cropping patterns"

Fig. 2

Phenology information atlas for croplands"

Fig. 3

Information remapping rules"

Fig. 4

Three-dimensional scatter points on different combinations of phenological features a: For different cropping patterns and trees or abandoned croplands on GS1-TG1-GS2; b: For Summer-and-Autumn double-cropping, double-cropping paddy-rice and trees or abandoned croplands on GS2-TG2-GS3"

Fig. 5

Cropping pattern spectrum on the Jianghan Plain A: The false color composite image of three phenological periods (GS1-TG1-GS2); B: Triangular RGB color space"

Fig. 6

Bayesian Network model for cropping patterns mapping CP: Represents cropping pattern node. SingleSpring: Spring single-cropping; SingleSummer: Summer single-cropping; DoubleSpringSummer: Spring-and- Summer double-cropping; DoubleSummerAutumn: Summer-and-Autumn double-cropping; DoubleCroppingRice: Double-cropping paddy-rice. GS1, TG1, GS2, TG2 and GS3 represent the features of the key phenological periods respectively"

Table 1

Probability map of the child nodes"

GS1 TG1 GS2 TG2 GS3
春单季 Spring single-cropping
夏单季 Summer single-cropping
双季稻 Double-cropping paddy-rice
春夏双季 Spring-and-Summer double-cropping
夏秋双季 Summer-and-Autumn double-cropping

Fig. 7

Schematic diagram of knowledge probability coding for vegetation index states"

Fig. 8

Forward and backword inference of Bayesian Network a: Forward inference: when the parent node is specified as "DoubleSpringSummer", the probability is passed to the child node, and GS1-TG1-GS2 changes to a state combination of "high-low-high". Since TG2 and GS3 nodes are only effective for DoubleCroppingRice, its probability does not change at this time. b: Backword inference: when the combination of GS1-TG1-GS2 is specified as "high-low-high", the probability is transferred to the parent node, and the probability of "DoubleSpringSummer" is the highest, reaching 93.8%"

Fig. 9

Comparison between the false color composite images of phenological features and the cropping pattern map in the validation areas a-e correspond to the validation areas A-E. a1-e1 are the false-color composite images of the three phenological features; a2-e2 are the cropping pattern maps"

Table 2

Accuracies for the cropping patterns in 2017"

类型
Type		 春单季
Spring
single-cropping		 夏单季
Summer
single-cropping		 春夏双季
Spring-and-Summer
double-cropping		 双季稻
Double-cropping
paddy-rice		 夏秋双季
Summer-and-Autumn
double-cropping		 其他
Others		 总计
Total		 用户精度
User’s
accuracy (%)
春单季
Spring single-cropping		 47 1 1 0 0 1 50 94.00
夏单季
Summer single-cropping		 0 190 1 6 1 11 209 90.91
春夏双季
Spring-and-Summer
double-cropping		 3 13 244 0 0 0 260 93.85
双季稻
Double-cropping
paddy-rice		 0 0 0 84 2 0 86 97.67
夏秋双季
Summer-and-Autumn
double-cropping		 0 0 0 7 73 2 82 89.02
其他 Others 1 24 10 4 2 164 205 80.00
总计 Total 51 228 256 101 78 178 892
生产者精度
Producer’s accuracy (%)		 92.16 83.33 95.31 83.17 93.59 92.13 OA = 0.899
Kappa=0.872

Table 3

Accuracies for the cropping patterns in 2018"

类型
Type		 春单季
Spring
single-cropping		 夏单季
Summer
single-cropping		 春夏双季
Spring-and-Summer
double-cropping		 双季稻
Double-cropping
paddy-rice		 夏秋双季
Summer-and-Autumn
double-cropping		 其他
Others		 总计
Total		 用户精度
User’s
accuracy (%)
春单季
Spring single-cropping		 45 0 0 0 0 0 45 100.00
夏单季
Summer single-cropping		 0 197 3 2 0 12 214 92.06
春夏双季
Spring-and-Summer
double-cropping		 3 1 235 0 1 3 243 96.71
双季稻
Double-cropping
paddy-rice		 0 0 0 85 0 0 85 100.00
夏秋双季
Summer-and-Autumn
double-cropping		 0 0 0 1 123 3 127 96.85
其他 Others 4 4 0 14 7 143 172 83.14
总计 Total 52 202 238 102 131 161 886
生产者精度
Producer’s accuracy (%)		 86.54 97.52 98.74 83.33 93.89 88.82 OA = 0.935
Kappa=0.918

Table 4

Accuracies for the cropping patterns in 2019"

类型
Type		 春单季
Spring
single-cropping		 夏单季
Summer
single-cropping		 春夏双季
Spring-and-Summer
double-cropping		 双季稻
Double-cropping
paddy-rice		 夏秋双季
Summer-and-Autumn
double-cropping		 其他
Others		 总计
Total		 用户精度
User’s
accuracy (%)
春单季
Spring single-cropping		 41 0 1 0 0 0 42 97.62
夏单季
Summer single-cropping		 0 402 6 1 0 14 423 95.04
春夏双季
Spring-and-Summer
double-cropping		 1 3 498 0 1 0 503 99.01
双季稻
Double-cropping
paddy-rice		 0 0 0 87 0 1 88 98.86
夏秋双季
Summer-and-Autumn
double-cropping		 0 0 0 1 120 5 126 95.24
其他 Others 0 9 2 7 9 152 179 84.92
总计 Total 42 414 507 96 130 172 1361
生产者精度
Producer’s accuracy (%)		 97.62 97.10 98.22 90.63 92.31 88.37 OA = 0.955
Kappa=0.939

Table 5

Accuracies for the cropping patterns in 2020"

类型
Type		 春单季
Spring
single-cropping		 夏单季
Summer
single-cropping		 春夏双季
Spring-and-Summer
double-cropping		 双季稻
Double-cropping
paddy-rice		 夏秋双季
Summer-and-Autumn
double-cropping		 其他
Others		 总计
Total		 用户精度
User’s
accuracy (%)
春单季
Spring single-cropping		 67 0 10 0 0 2 79 84.81
夏单季
Summer single-cropping		 0 329 2 9 0 3 343 95.92
春夏双季
Spring-and-Summer
double-cropping		 0 3 424 0 0 1 428 99.07
双季稻
Double-cropping
paddy-rice		 0 0 0 98 0 0 98 100.00
夏秋双季
Summer-and-Autumn
double-cropping		 1 0 1 0 164 4 170 96.47
其他 Others 1 9 11 5 7 174 207 84.06
总计 Total 69 341 448 112 171 184 1325
生产者精度
Producer’s accuracy (%)		 97.10 96.48 94.64 87.50 95.91 94.57 OA = 0.948
Kappa=0.933

Table 6

Accuracies for the cropping patterns in 2021"

类型
Type		 春单季
Spring
single-cropping		 夏单季
Summer
single-cropping		 春夏双季
Spring-and-Summer
double-cropping		 双季稻
Double-cropping
paddy-rice		 夏秋双季
Summer-and-Autumn
double-cropping		 其他
Others		 总计
Total		 用户精度
User’s
accuracy (%)
春单季
Spring single-cropping		 51 0 3 0 0 1 55 92.73
夏单季
Summer single-cropping		 0 431 47 1 8 25 512 84.18
春夏双季
Spring-and-Summer
double-cropping		 1 3 611 0 1 11 627 97.45
双季稻
Double-cropping
paddy-rice		 0 0 1 85 12 2 100 85.00
夏秋双季
Summer-and-Autumn
double-cropping		 0 1 3 2 199 7 212 93.87
其他 Others 2 30 18 15 13 205 283 72.44
总计 Total 54 465 683 103 233 251 1789
生产者精度
Producer’s accuracy (%)		 94.44 92.69 89.46 82.52 85.41 81.67 OA = 0.884
Kappa=0.846

Fig. 10

Spatial distributions and area proportions of cropping patterns on the Jianghan Plain from 2017 to 2021"

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